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Record W2086643449 · doi:10.1080/02656730410001668366

Infrared thermographic SAR measurements of interstitial hyperthermia applicators: errors due to thermal conduction and convection

2004· article· en· W2086643449 on OpenAlexaff
A. S. Gladman, Sean M. Davidson, Anthony Easty, M. Joy, M.D. Sherar

Bibliographic record

VenueInternational Journal of Hyperthermia · 2004
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsToronto General HospitalPrincess Margaret Cancer CentreUniversity Health NetworkLondon Health Sciences CentreUniversity of Toronto
FundersNational Cancer Institute
KeywordsThermal conductionMaterials scienceConvectionHeat transferThermographyImaging phantomConvective heat transferPulse durationThermalThermal diffusivityOpticsBiomedical engineeringInfraredMechanicsComposite materialMeteorologyMedicinePhysicsThermodynamics

Abstract

fetched live from OpenAlex

Thermal conduction and convection were examined as sources of error in thermographically measured SAR patterns of an interstitial microwave hyperthermia applicator. Measurements were performed in a layered block of muscle-equivalent phantom material using an infrared thermographic technique with varying heating duration. There was a 52.7% reduction in maximum SAR and 75.5% increase in 50% iso-SAR contour area for a 60-s heating duration relative to a 10-s heating duration. A finite element model of heat transfer in an homogeneous medium was used to model conductive and convective heat transfer during the thermographic measurement. Thermal conduction artefacts were found to significantly distort thermographically measured SAR patterns. Convective cooling, which occurs when phantom layers are exposed for thermal image acquisition, was found to significantly affect the magnitude, but not the spatial distribution, of thermographically measured SAR patterns. Results from this investigation suggest that the thermal diffusion artefacts can be minimized if the duration of the applied power pulse is restricted to 10 s or less.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.237
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2004
Admission routes1
Has abstractyes

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